Software Jobs in India | EDA |

1. EDA can be used to explore and understand the job market for software jobs in India. By analyzing job posting data from various job portals and websites, we can gain insights into the current state of the job market, including which technologies and skills are in high demand, which cities and regions have the most job opportunities, and which companies are hiring the most.

2. EDA can help us identify trends and patterns in the job market, such as the emergence of new technologies or the rise of certain types of job roles. By visualizing the data using graphs and charts, we can better understand the dynamics of the job market and make informed decisions about our career paths.

3. EDA can also help us identify potential gaps and opportunities in the job market. For example, if we notice that there are many job postings for a certain technology but few candidates with that skillset, we may consider learning that technology to increase our chances of landing a job.

3. EDA can be used to compare the job market across different cities and regions in India. By analyzing data from multiple sources, we can identify which cities have the highest salaries, the most job opportunities, and the most competitive job markets.

4. Finally, EDA can be used to track changes in the job market over time. By analyzing historical data, we can identify trends and patterns that can help us predict future changes in the job market and make informed decisions about our career paths.

import pandas as pd

df = pd.read_csv("/Users/soumyaprasadchandra/Downloads/Salary_Dataset_with_Extra_Features.csv")

df.head()

	Rating	Company Name	Job Title	Salary	Salaries Reported	Location	Employment Status	Job Roles
0	3.8	Sasken	Android Developer	400000	3	Bangalore	Full Time	Android
1	4.5	Advanced Millennium Technologies	Android Developer	400000	3	Bangalore	Full Time	Android
2	4.0	Unacademy	Android Developer	1000000	3	Bangalore	Full Time	Android
3	3.8	SnapBizz Cloudtech	Android Developer	300000	3	Bangalore	Full Time	Android
4	4.4	Appoids Tech Solutions	Android Developer	600000	3	Bangalore	Full Time	Android

df.tail()

	Rating	Company Name	Job Title	Salary	Salaries Reported	Location	Employment Status	Job Roles
22765	4.7	Expert Solutions	Web Developer	200000	1	Bangalore	Full Time	Web
22766	4.0	Nextgen Innovation Labs	Web Developer	300000	1	Bangalore	Full Time	Web
22767	4.1	Fresher	Full Stack Web Developer	192000	13	Bangalore	Full Time	Web
22768	4.1	Accenture	Full Stack Web Developer	300000	7	Bangalore	Full Time	Web
22769	3.8	Thomson Reuters	Associate Web Developer	300000	7	Bangalore	Full Time	Web

df.shape

(22770, 8)

df.columns

Index(['Rating', 'Company Name', 'Job Title', 'Salary', 'Salaries Reported',
       'Location', 'Employment Status', 'Job Roles'],
      dtype='object')

df.duplicated().sum()

0

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 22770 entries, 0 to 22769
Data columns (total 8 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   Rating             22770 non-null  float64
 1   Company Name       22770 non-null  object 
 2   Job Title          22770 non-null  object 
 3   Salary             22770 non-null  int64  
 4   Salaries Reported  22770 non-null  int64  
 5   Location           22770 non-null  object 
 6   Employment Status  22770 non-null  object 
 7   Job Roles          22770 non-null  object 
dtypes: float64(1), int64(2), object(5)
memory usage: 1.4+ MB

df.describe()

	Rating	Salary	Salaries Reported
count	22770.000000	2.277000e+04	22770.000000
mean	3.918213	6.953872e+05	1.855775
std	0.519675	8.843990e+05	6.823668
min	1.000000	2.112000e+03	1.000000
25%	3.700000	3.000000e+05	1.000000
50%	3.900000	5.000000e+05	1.000000
75%	4.200000	9.000000e+05	1.000000
max	5.000000	9.000000e+07	361.000000

df.nunique()

Rating                  41
Company Name         11261
Job Title             1080
Salary                 316
Salaries Reported       82
Location                10
Employment Status        4
Job Roles               11
dtype: int64

objects_cols = list(df.select_dtypes(include=['object']).columns)
num_cols = list(df.select_dtypes(include=['int64', 'float64']).columns)

print("object type columns", objects_cols)
print("Numerical type columns:", num_cols)

object type columns ['Company Name', 'Job Title', 'Location', 'Employment Status', 'Job Roles']
Numerical type columns: ['Rating', 'Salary', 'Salaries Reported']

df['Company Name'].unique()

array(['Sasken', 'Advanced Millennium Technologies', 'Unacademy', ...,
       'Unicon Systems', 'Expert Solutions', 'Nextgen Innovation Labs'],
      dtype=object)

df['Company Name'].value_counts()

Tata Consultancy Services         271
Amazon                            184
Infosys                           169
Accenture                         150
Cognizant Technology Solutions    144
                                 ... 
Talent Anywhere                     1
WisdmLabs                           1
Softdel                             1
Dentsu                              1
Nextgen Innovation Labs             1
Name: Company Name, Length: 11261, dtype: int64

df['Job Title'].unique()

array(['Android Developer', 'Android Developer - Intern',
       'Android Developer - Contractor', ..., 'Web Developer Contractor',
       'Full Stack Web Developer', 'Associate Web Developer'],
      dtype=object)

df['Job Title'].value_counts()

Software Development Engineer          2351
Android Developer                      2029
Software Development Engineer (SDE)    1614
Front End Developer                    1412
Test Engineer                          1314
                                       ... 
Java Andriod Developer                    1
Java Deceloper                            1
Java/J2EE Programmer                      1
Java SOA Developer                        1
Associate Web Developer                   1
Name: Job Title, Length: 1080, dtype: int64

df['Location'].unique()

array(['Bangalore', 'Chennai', 'Hyderabad', 'New Delhi', 'Pune', 'Jaipur',
       'Kerala', 'Kolkata', 'Madhya Pradesh', 'Mumbai'], dtype=object)

df['Location'].value_counts()

Bangalore         8264
Hyderabad         4467
New Delhi         4176
Chennai           2458
Pune              2134
Mumbai             749
Kolkata            178
Madhya Pradesh     155
Kerala             108
Jaipur              81
Name: Location, dtype: int64

import matplotlib.pyplot as plt
import seaborn as sns

import warnings
warnings.filterwarnings('ignore')

import numpy as np

plt.figure(figsize=(15,6))
sns.countplot(df['Location'], data=df, palette='hls')
plt.show()

plt.figure(figsize=(30,20))
plt.pie(df['Location'].value_counts(), labels=df['Location'].value_counts().index, autopct='%1.2f%%',
       )

#hfont = {'fontname':'serif', 'weight':'bold'}
plt.title('Location', size=20)
plt.show()

import plotly.express as px

value_counts = df['Location'].value_counts()
fig = px.pie(names = value_counts.index, values = value_counts.values)
fig.update_layout(
    title = 'Pie Chart of Location',
    title_x = 0.5)

fig.show()

df['Employment Status'].unique()

array(['Full Time', 'Intern', 'Contractor', 'Trainee'], dtype=object)

plt.figure(figsize=(15,6))
sns.countplot(df['Employment Status'], data = df, palette = 'hls')
plt.show()

plt.figure(figsize=(15,6))
plt.pie(df['Employment Status'].value_counts(), labels=df['Employment Status'].value_counts(), autopct='1f.2f%%')

plt.title('Employment Status', size=30)
plt.show()

value_counts =df['Employment Status'].value_counts()
fig = px.pie(names = value_counts.index, values= value_counts.values)
fig.update_layout(title='Pie Chart of Employment Status',
                 title_x=0.5)
fig.show()

df['Job Title'].unique()

array(['Android Developer', 'Android Developer - Intern',
       'Android Developer - Contractor', ..., 'Web Developer Contractor',
       'Full Stack Web Developer', 'Associate Web Developer'],
      dtype=object)

df['Job Roles'].value_counts()

SDE         8183
Android     2945
Frontend    2163
Java        1858
Testing     1740
IOS         1631
Backend     1194
Web          999
Python       947
Database     865
Mobile       245
Name: Job Roles, dtype: int64

plt.figure(figsize=(15,6))
sns.countplot(df['Job Roles'], data = df, palette = 'hls')
plt.show()

plt.figure(figsize=(30,20))
plt.pie(df['Job Roles'].value_counts(), labels=df['Job Roles'].value_counts().index, autopct='1%.2f%%')
plt.title('Job Roles', size=30)
plt.show()

value_counts = df['Job Roles'].value_counts()
fig = px.pie(names=value_counts.index, values=value_counts.values)
fig.update_layout(title='Pie Chart of Job Roles',
                 title_x = 0.5)
fig.show()

for i in num_cols:
    plt.figure(figsize=(15,6))
    sns.histplot(df[i], kde=True, bins= 20, palette='hls')
    plt.xticks(rotation=90)
    plt.show()

for i in num_cols:
    plt.figure(figsize=(15,6))
    sns.distplot(df[i], kde=True, bins= 20)
    plt.xticks(rotation=90)
    plt.show()

for k in num_cols:
    plt.figure(figsize=(15,6))
    sns.boxplot(df[k], data=df, palette='hls')
    plt.xticks(rotation=90)
    plt.show()

for col in num_cols:
    fig = px.box(df, y = col)
    fig.show()

for j in num_cols:
    plt.figure(figsize=(15,6))
    sns.violinplot(df[j], data=df, palette='hls')
    plt.xticks(rotation=90)
    plt.show()

for col in num_cols:
    fig = px.violin(df, y=col)
    fig.show()

for i in num_cols:
    if j in num_cols:
        if i!=j:
            plt.figure(figsize=(15,6))
            sns.lineplot(x=df[i], y=df[j], data=df, ci=None, palette='hls')
            plt.xticks(rotation=90)
            plt.show()

for i in num_cols:
    for j in num_cols:
        if i!=j:
            plt.figure(figsize=(15,6))
            sns.scatterplot(x=df[i], y = df[j], data=df, ci=None, palette='hls')
            plt.xticks(rotation=90)
            plt.show()

from wordcloud import WordCloud

data = ' '.join(df['Company Name'].values)
mycloud = WordCloud(width=800, height=400,
                   margin=0,
                   background_color = 'black',
                   colormap='plasma').generate(data)
plt.figure(figsize=(15,6))
plt.imshow(mycloud, interpolation='bilinear')
plt.axis('off')
plt.margins(x=0,y=0)
plt.show()

import plotly.express as px
from wordcloud import WordCloud

data = ' '.join(df["Company Name"].values)

wordcloud = WordCloud(width=800, height=400, background_color='black').generate(data)

fig = px.imshow(wordcloud, template='ggplot2')
fig.update_layout(title_text='Word Cloud of Company Names', title_x = 0.5)
fig.update_xaxes(showticklabels=False).update_yaxes(showticklabels=False)
fig.show()

companies = df['Company Name'].value_counts().head(50)
plt.figure(figsize=(12,15))
sns.barplot(y=companies.index, x = companies.values, palette='plasma')
plt.show()

df.groupby('Company Name')['Company Name'].sum().head(20)

Company Name
(X,Y,Z) Architecture & Design                          (X,Y,Z) Architecture & Design
(no)name                            (no)name(no)name(no)name(no)name(no)name(no)name
-                                                                                 --
....                                                                            ....
.Kreate                                                               .Kreate.Kreate
1 Crore Projects                                                    1 Crore Projects
10                                                                                10
100Plus (India)                                                      100Plus (India)
10Decoders Consultancy Services    10Decoders Consultancy Services10Decoders Cons...
11Signals Technologies                                        11Signals Technologies
17544                                                                          17544
1985                                                                            1985
1Digitals                                                                  1Digitals
1Gen                                                                        1Gen1Gen
1K Kirana Bazaar                                    1K Kirana Bazaar1K Kirana Bazaar
1Tab                                                                            1Tab
1mg                                                         1mg1mg1mg1mg1mg1mg1mg1mg
1st Risk Solutions                                                1st Risk Solutions
2018                                                                            2018
20K Group                                                                  20K Group
Name: Company Name, dtype: object

12510
22502
22817
15228
12254
12863